Recently, green energy generation systems, such as wind power generation systems that are capable of harvesting wind power to be used for producing electricity without emissions, beginning to attract more and more attentions as they can be used as clean and safe renewable power sources. Taking the power generation system of the prime mover for instance, it can be designed to operate in either a constant-speed mode or a variable-speed mode for producing electricity through the conversion of power electronic converters. Among which, the variable-speed generation system is more attractive than the fixed-speed system because of the improvement in energy production and the reduction of the flicker problem. In addition, the turbine in the variable-speed generation system can be operated at the maximum power operating point for various speeds by adjusting the shaft speed optimally to achieve maximum efficiency. All these characteristics are advantages of the variable-speed energy conversion systems. Nevertheless, in order to achieve the maximum power control, some control schemes have been studied.
Many generators of research interests and for practical use in generation are induction machines with wound-rotor or cage-type rotor. Recently, the interest in permanent magnet synchronous generator (PMSG) is increasing. The desirable features of the PMSG are its compact structure, high air-gap flux density, high power density, high torque-to-inertia ratio, and high torque capability. Moreover, compared with an induction generator, a PMSG has the advantage of a higher efficiency, due to the absence of rotor losses and lower no-load current below the rated speed; and its decoupling control performance is much less sensitive to the parameter variations of the generator. Therefore, using a PMSG, a high-performance variable-speed generation system with high efficiency and high controllability can be expected.
There are already many related studies available today. To name a few, one such prior study proposed a power generation system with neural network principles applied for speed estimation and PI control for maximum power extraction, using which the mechanical power of the turbine can be well tracked for both dynamic and steady state, but the power deviation and speed tracking errors are large with transient response for almost 20 seconds. Another prior study proposed the development of a cascaded nonlinear controller for a variable-speed wind turbine equipped with a DFIG, but the rotor speed errors are large with efficiency around 70%. Further, there is a study proposed an advanced hill-climb searching method taking into account the wind-turbine inertia. However, it required an additional intelligent memory method with an on-line training process, and maximum error of power coefficient is about 23%. In addition, another prior study proposed an output maximization control without mechanical sensors such as the speed sensor and position sensor, but the ac power output efficiency is only around 80%. Furthermore, there are three sensorless control methods, which are the wind prediction, fixed voltage scheme for inverter, and current-controlled inverter, presented in is further another prior study, but it is disadvantageous in that: the fixed voltage scheme does not vary with the load to match the maximum power line of the wind turbine generator, and results in low conversion efficiency when the wind speed is above or below the given range attained. Moreover, there are two methods developed in another prior study which are provided to adjust the aerodynamic power: pitch and generator load control, both of which are employed to regulate the operation of the wind turbine, but are disadvantageous in that: the power coefficient deviation is too large.
Therefore, it is in need of a novel hybrid intelligent control system and algorithm for a power generating apparatus, such as a PMSG, capable of optimizing the performance of the power generating apparatus by performing a speed control using a sliding mode controller combined with fuzzy inference mechanism and adaptive algorithm, and also by performing a pitch control upon a turbine coupled to the power generating apparatus using pitch controller embedded with a RBFN algorithm. Moreover, in the sliding mode controller, a switching surface with an integral operation is designed. Operationally, when the sliding mode occurs, the system dynamic behaves as a robust state feedback control system, and in a general sliding mode control, the upper bound of uncertainties, including parameter variations and external mechanical disturbance, must be available. However, the bound of the uncertainties is difficult to obtain in advance for practical applications. Thus, a fuzzy sliding speed controller is investigated to resolve the above difficulty, in which a simple fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, to reduce the control effort of the sliding mode speed controller, the fuzzy inference mechanism is improved by adapting the center of the membership functions to estimate the optimal bound of uncertainties.